Instructions to use julep-ai/samantha-1-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use julep-ai/samantha-1-alpha with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="julep-ai/samantha-1-alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("julep-ai/samantha-1-alpha")
model = AutoModelForCausalLM.from_pretrained("julep-ai/samantha-1-alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use julep-ai/samantha-1-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "julep-ai/samantha-1-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "julep-ai/samantha-1-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/julep-ai/samantha-1-alpha

SGLang

How to use julep-ai/samantha-1-alpha with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "julep-ai/samantha-1-alpha" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "julep-ai/samantha-1-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "julep-ai/samantha-1-alpha" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "julep-ai/samantha-1-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use julep-ai/samantha-1-alpha with Docker Model Runner:
```
docker model run hf.co/julep-ai/samantha-1-alpha
```

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Samantha

Technical notes

This model is trained on a specialized dataset and uses special sentinel tokens to demarcate conversations.

Usage

For usage, you can refer to the chat.py file in this repo for an example.

Concepts

Each conversation consists of n "sections"
Each section can be one of:
- me: The model
- person: The speaker
- situation: relevant background information to set the context of the conversation
- thought: Thoughts generated by the model for parsing intermediate steps etc
- information: External information added into the context by the system running the model
The model and speaker sections can optionally include a name like me (Samantha) or person (Dmitry)

Sentinel Tokens

<|im_start|> token marks the start of a "section"
<|im_end|> token marks the end of a "section".

Example

<|im_start|>situation
I am talking to Diwank. I want to ask him about his food preferences.<|im_end|>
<|im_start|>person (Diwank)
Hey Samantha! What do you want to talk about?<|im_end|>
<|im_start|>me (Samantha)

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